Graph Semi-Supervised learning is an important data analysis tool, where given a graph and a set of labeled nodes, the aim is to infer the labels to the remaining unlabeled nodes. In this paper, we start by considering an optimization-based formulation of the problem for an undirected graph, and then we extend this formulation to multilayer hypergraphs. We solve the problem using different coordinate descent approaches and compare the results with the ones obtained by the classic gradient descent method. Experiments on synthetic and real-world datasets show the potential of using coordinate descent methods with suitable selection rules.

翻译：半支持学习图是一个重要的数据分析工具, 给定一个图表和一组标签节点, 目的是将标签推到其余未标的节点上。 在本文中, 我们首先考虑对一个未标的图进行基于优化的问题配方, 然后将这一配方扩展至多层高音。 我们使用不同的协调下降方法解决问题, 并将结果与经典梯度下降方法获得的结果进行比较。 合成和真实世界数据集实验显示使用协调下降方法与合适的选择规则的潜力 。